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00300 00301 00302 00303 <span class="comment">/*</span>00304 <span class="comment"> * Vector / Matrix types</span>00305 <span class="comment"> * Finally the definitions !</span>00306 <span class="comment"> */</span>00307 <span class="keyword">using</span> detail::FMVec; <span class="comment">// Template class for template parameter matching</span>00308 <span class="keyword">using</span> detail::FMMatrix;00309 00310 <span class="comment">// Default types</span><a name="l00311"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a1">00311</a> <span class="keyword">typedef</span> FMVec<detail::BaseVector> <a class="code" href="namespaceBayesian__filter__matrix.html#a1">Vec</a>;<a name="l00312"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a2">00312</a> <span class="keyword">typedef</span> FMMatrix<detail::BaseRowMatrix> <a class="code" href="namespaceBayesian__filter__matrix.html#a2">RowMatrix</a>;<a name="l00313"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a3">00313</a> <span class="keyword">typedef</span> <a class="code" href="namespaceBayesian__filter__matrix.html#a2">RowMatrix</a> <a class="code" href="namespaceBayesian__filter__matrix.html#a3">Matrix</a>;<a name="l00314"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a4">00314</a> <span class="keyword">typedef</span> FMMatrix<detail::BaseColMatrix> <a class="code" href="namespaceBayesian__filter__matrix.html#a4">ColMatrix</a>;<a name="l00315"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a5">00315</a> <span class="keyword">typedef</span> FMMatrix<detail::SymMatrixWrapper<detail::BaseRowMatrix> > <a class="code" href="namespaceBayesian__filter__matrix.html#a5">SymMatrix</a>;<a name="l00316"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a6">00316</a> <span class="keyword">typedef</span> FMMatrix<detail::BaseUpperTriMatrix> <a class="code" href="namespaceBayesian__filter__matrix.html#a6">UTriMatrix</a>;<a name="l00317"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a7">00317</a> <span class="keyword">typedef</span> FMMatrix<detail::BaseLowerTriMatrix> <a class="code" href="namespaceBayesian__filter__matrix.html#a7">LTriMatrix</a>;<a name="l00318"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a8">00318</a> <span class="keyword">typedef</span> FMMatrix<detail::BaseDiagMatrix> <a class="code" href="namespaceBayesian__filter__matrix.html#a8">DiagMatrix</a>;00319 00320 <span class="comment">// Explicitly dense types</span><a name="l00321"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a9">00321</a> <span class="keyword">typedef</span> FMVec<detail::BaseDenseVector> <a class="code" href="namespaceBayesian__filter__matrix.html#a9">DenseVec</a>;<a name="l00322"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a10">00322</a> <span class="keyword">typedef</span> FMMatrix<detail::BaseDenseRowMatrix> <a class="code" href="namespaceBayesian__filter__matrix.html#a10">DenseRowMatrix</a>;<a name="l00323"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a11">00323</a> <span class="keyword">typedef</span> <a class="code" href="namespaceBayesian__filter__matrix.html#a10">DenseRowMatrix</a> <a class="code" href="namespaceBayesian__filter__matrix.html#a11">DenseMatrix</a>;<a name="l00324"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a12">00324</a> <span class="keyword">typedef</span> FMMatrix<detail::BaseDenseColMatrix> <a class="code" href="namespaceBayesian__filter__matrix.html#a12">DenseColMatrix</a>;<a name="l00325"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a13">00325</a> <span class="keyword">typedef</span> FMMatrix<detail::SymMatrixWrapper<detail::BaseDenseRowMatrix> > <a class="code" href="namespaceBayesian__filter__matrix.html#a13">DenseSymMatrix</a>;<a name="l00326"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a14">00326</a> <span class="keyword">typedef</span> FMMatrix<detail::BaseDenseUpperTriMatrix> <a class="code" href="namespaceBayesian__filter__matrix.html#a14">DenseUTriMatrix</a>;<a name="l00327"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a15">00327</a> <span class="keyword">typedef</span> FMMatrix<detail::BaseDenseLowerTriMatrix> <a class="code" href="namespaceBayesian__filter__matrix.html#a15">DenseLTriMatrix</a>;<a name="l00328"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a16">00328</a> <span class="keyword">typedef</span> FMMatrix<detail::BaseDenseDiagMatrix> <a class="code" href="namespaceBayesian__filter__matrix.html#a16">DenseDiagMatrix</a>;00329 00330 <span class="comment">// Explicitly sparse types (any of the gappy types)</span>00331 <span class="preprocessor">#ifdef BAYES_FILTER_GAPPY</span>00332 <span class="preprocessor"></span><span class="keyword">typedef</span> FMVec<detail::BaseSparseVector> SparseVec;00333 <span class="keyword">typedef</span> FMMatrix<detail::BaseDenseRowMatrix> SparseRowMatrix;00334 <span class="keyword">typedef</span> SparseRowMatrix SparseMatrix;00335 <span class="keyword">typedef</span> FMMatrix<detail::BaseSparseColMatrix> SparseColMatrix;00336 <span class="keyword">typedef</span> FMMatrix<detail::SymMatrixWrapper<detail::BaseSparseRowMatrix> > SparseSymMatrix;00337 <span class="preprocessor">#endif</span>00338 <span class="preprocessor"></span>00339 00340 <span class="comment">/*</span>00341 <span class="comment"> * Matrix Adaptors, simply hide the uBLAS details</span>00342 <span class="comment"> */</span>00343 <span class="keyword">template</span> <<span class="keyword">class</span> M>00344 <span class="keyword">const</span> ublas::triangular_adaptor<const M, ublas::upper><a name="l00345"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a51">00345</a> <a class="code" href="namespaceBayesian__filter__matrix.html#a51">UpperTri</a>(<span class="keyword">const</span> M& m)00346 <span class="comment">/*</span>00347 <span class="comment"> * View Upper triangle of m</span>00348 <span class="comment"> * ISSUE VC7 cannot cope with UTriMatrix::functor1_type</span>00349 <span class="comment"> */</span>00350 {00351 <span class="keywordflow">return</span> ublas::triangular_adaptor<const M, ublas::upper>(m);00352 }00353 00354 <span class="keyword">template</span> <<span class="keyword">class</span> M>00355 <span class="keyword">const</span> ublas::triangular_adaptor<const M, ublas::lower><a name="l00356"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a52">00356</a> <a class="code" href="namespaceBayesian__filter__matrix.html#a52">LowerTri</a>(<span class="keyword">const</span> M& m)00357 <span class="comment">/*</span>00358 <span class="comment"> * View Lower triangle of m</span>00359 <span class="comment"> */</span>00360 {00361 <span class="keywordflow">return</span> ublas::triangular_adaptor<const M, ublas::lower>(m);00362 }00363 00364 00365 00366 <span class="comment">/*</span>00367 <span class="comment"> * Matrix Support Operations</span>00368 <span class="comment"> */</span>00369 <span class="keyword">template</span> <<span class="keyword">class</span> Base>00370 ublas::matrix_vector_range<FMMatrix<Base> ><a name="l00371"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a53">00371</a> <a class="code" href="namespaceBayesian__filter__matrix.html#a53">diag</a>(FMMatrix<Base>& M, size_t n)00372 { <span class="comment">// Return a vector proxy to the first n diagonal elements of M</span>00373 <span class="keywordflow">return</span> ublas::matrix_vector_range<FMMatrix<Base> >(M, ublas::range(0,n), ublas::range(0,n));00374 }00375 00376 <span class="keyword">template</span> <<span class="keyword">class</span> Base>00377 <span class="keyword">const</span> ublas::matrix_vector_range<const FMMatrix<Base> ><a name="l00378"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a54">00378</a> <a class="code" href="namespaceBayesian__filter__matrix.html#a53">diag</a>(<span class="keyword">const</span> FMMatrix<Base>& M, size_t n)00379 { <span class="comment">// Return a vector proxy to the first n diagonal elements of M</span>00380 <span class="keywordflow">return</span> ublas::matrix_vector_range<const FMMatrix<Base> >(M, ublas::range(0,n), ublas::range(0,n));00381 }00382 00383 <span class="keyword">template</span> <<span class="keyword">class</span> Base>00384 ublas::matrix_vector_range<FMMatrix<Base> ><a name="l00385"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a55">00385</a> <a class="code" href="namespaceBayesian__filter__matrix.html#a53">diag</a>(FMMatrix<Base>& M)00386 { <span class="comment">// Return a vector proxy to the diagonal elements of M</span>00387 <span class="keyword">const</span> size_t common_size = std::min(M.size1(),M.size2());00388 <span class="keywordflow">return</span> ublas::matrix_vector_range<FMMatrix<Base> >(M, ublas::range(0,common_size), ublas::range(0,common_size));00389 }00390 00391 <span class="keyword">template</span> <<span class="keyword">class</span> Base>00392 <span class="keyword">const</span> ublas::matrix_vector_range<const FMMatrix<Base> ><a name="l00393"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a56">00393</a> <a class="code" href="namespaceBayesian__filter__matrix.html#a53">diag</a>(<span class="keyword">const</span> FMMatrix<Base>& M)00394 { <span class="comment">// Return a vector proxy to the diagonal elements of M</span>00395 <span class="keyword">const</span> size_t common_size = std::min(M.size1(),M.size2());00396 <span class="keywordflow">return</span> ublas::matrix_vector_range<const FMMatrix<Base> >(M, ublas::range(0,common_size), ublas::range(0,common_size));00397 }00398 00399 <span class="keyword">template</span> <<span class="keyword">class</span> Base><a name="l00400"></a><a class="code" href="namespaceBayesian__filter__matrix.html#a57">00400</a> <span class="keywordtype">void</span> <a class="code" href="namespaceBayesian__filter__matrix.html#a57">identity</a>(FMMatrix<Base>& I)00401 { <span class="comment">// Set I to generalised Identity matrix. Clear I and set diag(I) to one</span>00402 I.clear();00403 <span class="comment">// Set common diagonal elements</span>00404 size_t common_size = std::min(I.size1(),I.size2());00405 <span class="keyword">typedef</span> <span class="keyword">typename</span> Base::value_type Base_value_type;00406 <a class="code" href="namespaceBayesian__filter__matrix.html#a53">diag</a>(I) = ublas::scalar_vector<Base_value_type>(common_size, Base_value_type(1));00407 }00408 00409 00410 00411 <span class="comment">/*</span>00412 <span class="comment"> * Symmetric Positive (Semi) Definate multiplication: X*S*X' and X'*S*X</span>00413 <span class="comment"> * The name is slightly misleading. The result is actually only PD if S is PD</span>00414 <span class="comment"> * Algorithms are intended to exploit the symmerty of the result</span>00415 <span class="comment"> * and also where possible the row by row multiplication inherent in X*X'</span>00416 <span class="comment"> */</span>00417 00418 <span class="keyword">namespace </span>detail { <span class="comment">// mult_SPD now an implementation detail</span>00419 00420 <span class="keyword">template</span> <<span class="keyword">class</span> MatrixX><a name="l00421"></a><a class="code" href="namespaceBayesian__filter__matrix_1_1detail.html#a12">00421</a> <span class="keywordtype">void</span> <a class="code" href="namespaceBayesian__filter__matrix_1_1detail.html#a12">mult_SPD</a> (<span class="keyword">const</span> MatrixX& X, <span class="keyword">const</span> Vec& s, SymMatrix& P)00422 <span class="comment">/*</span>00423 <span class="comment"> * Symmetric Positive (Semi) Definate multiply: P += X*diag_matrix(s)*X'</span>00424 <span class="comment"> */</span>00425 {00426 Vec::const_iterator si, send = s.end();00427 <span class="keyword">typename</span> MatrixX::const_iterator1 Xa = X.begin1();00428 <span class="keyword">const</span> <span class="keyword">typename</span> MatrixX::const_iterator1 Xend = X.end1();00429 <span class="keyword">typename</span> MatrixX::const_iterator1 Xb;00430 00431 <span class="comment">// P(a,b) = X.row(a) * X.row(b)</span>00432 <span class="keywordflow">for</span> (; Xa != Xend; ++Xa) <span class="comment">// Iterate Rows</span>00433 {00434 <span class="keyword">typename</span> MatrixX::const_Row Xav = MatrixX::rowi(Xa);00435 Xb = Xa; <span class="comment">// Start at the row Xa only one triangle of symetric result required</span>00436 <span class="keywordflow">for</span> (; Xb != Xend; ++Xb)00437 {00438 SymMatrix::value_type p = 0; <span class="comment">// Tripple vector inner product</span>00439 <span class="keyword">typename</span> MatrixX::const_Row Xbv = MatrixX::rowi(Xb);00440 <span class="keywordflow">for</span> (si = s.begin(); si != send; ++si) {00441 Vec::size_type i = si.index();00442 p += Xav[i] * (*si) * Xbv[i];00443 }00444 P(Xa.index1(),Xb.index1()) += p;00445 }00446 }00447 }00448 00449 <span class="keyword">template</span> <<span class="keyword">class</span> MatrixX><a name="l00450"></a><a class="code" href="namespaceBayesian__filter__matrix_1_1detail.html#a13">00450</a> <span class="keywordtype">void</span> <a class="code" href="namespaceBayesian__filter__matrix_1_1detail.html#a13">mult_SPDT</a> (<span class="keyword">const</span> MatrixX& X, <span class="keyword">const</span> Vec& s, SymMatrix& P)00451 <span class="comment">/*</span>00452 <span class="comment"> * Symmetric Positive (Semi) Definate multiply: P += X'*diag_matrix(s)*X</span>⌨️ 快捷键说明
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